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      "metadata": {},
      "source": [
        "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
        "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/JavaScripts/Demos/EgyptClassification.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/JavaScripts/Demos/EgyptClassification.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/JavaScripts/Demos/EgyptClassification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install Earth Engine API and geemap\n",
        "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
        "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet.\n",
        "\n",
        "**Important note**: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60#issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Installs geemap package\n",
        "import subprocess\n",
        "\n",
        "try:\n",
        "    import geemap\n",
        "except ImportError:\n",
        "    print('geemap package not installed. Installing ...')\n",
        "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])\n",
        "\n",
        "# Checks whether this notebook is running on Google Colab\n",
        "try:\n",
        "    import google.colab\n",
        "    import geemap.eefolium as geemap\n",
        "except:\n",
        "    import geemap\n",
        "\n",
        "# Authenticates and initializes Earth Engine\n",
        "import ee\n",
        "\n",
        "try:\n",
        "    ee.Initialize()\n",
        "except Exception as e:\n",
        "    ee.Authenticate()\n",
        "    ee.Initialize()  "
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an interactive map \n",
        "The default basemap is `Google MapS`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "# Upsample MODIS landcover classification (250m) to Landsat\n",
        "# resolution (30m) using a supervised classifier.\n",
        "\n",
        "geometry = ee.Geometry.Polygon(\n",
        "        [[[29.972731783841393, 31.609824974226175],\n",
        "          [29.972731783841393, 30.110383818311096],\n",
        "          [32.56550522134139, 30.110383818311096],\n",
        "          [32.56550522134139, 31.609824974226175]]], {}, False)\n",
        "\n",
        "# Use the MCD12 land-cover as training data.\n",
        "collection = ee.ImageCollection('MODIS/006/MCD12Q1')\n",
        "# See the collection docs to get details on classification system.\n",
        "modisLandcover = collection \\\n",
        "    .filterDate('2001-01-01', '2001-12-31') \\\n",
        "    .first() \\\n",
        "    .select('LC_Type1') \\\n",
        "    .subtract(1)\n",
        "\n",
        "# A pallete to use for visualizing landcover images.  You can get this\n",
        "# from the properties of the collection.\n",
        "landcoverPalette = '05450a,086a10,54a708,78d203,009900,c6b044,dcd159,' + \\\n",
        "    'dade48,fbff13,b6ff05,27ff87,c24f44,a5a5a5,ff6d4c,69fff8,f9ffa4,1c0dff'\n",
        "# A set of visualization parameters using the landcover palette.\n",
        "landcoverVisualization = {'palette': landcoverPalette, 'min': 0, 'max': 16, 'format': 'png'}\n",
        "# Center over our region of interest.\n",
        "Map.centerObject(geometry, 11)\n",
        "# Draw the MODIS landcover image.\n",
        "Map.addLayer(modisLandcover, landcoverVisualization, 'MODIS landcover')\n",
        "\n",
        "# Load and filter Landsat data.\n",
        "l7 = ee.ImageCollection('LANDSAT/LE07/C01/T1') \\\n",
        "    .filterBounds(geometry) \\\n",
        "    .filterDate('2000-01-01', '2001-01-01')\n",
        "\n",
        "# Draw the Landsat composite, visualizing True color bands.\n",
        "landsatComposite = ee.Algorithms.Landsat.simpleComposite({\n",
        "  'collection': l7,\n",
        "  'asFloat': True\n",
        "})\n",
        "Map.addLayer(landsatComposite, {'min': 0, 'max': 0.3, 'bands': ['B3','B2','B1']}, 'Landsat composite')\n",
        "\n",
        "# Make a training dataset by sampling the stacked images.\n",
        "training = modisLandcover.addBands(landsatComposite).sample({\n",
        "  'region': geometry,\n",
        "  'scale': 30,\n",
        "  'numPixels': 1000\n",
        "})\n",
        "\n",
        "# Train a classifier using the training data.\n",
        "classifier = ee.Classifier.smileCart().train({\n",
        "  'features': training,\n",
        "  'classProperty': 'LC_Type1',\n",
        "})\n",
        "\n",
        "# Apply the classifier to the original composite.\n",
        "upsampled = landsatComposite.classify(classifier)\n",
        "\n",
        "# Draw the upsampled landcover image.\n",
        "Map.addLayer(upsampled, landcoverVisualization, 'Upsampled landcover')\n",
        "\n",
        "# Show the training area.\n",
        "Map.addLayer(geometry, {}, 'Training region', False)\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
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